We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

The somatic genomic landscape of glioblastoma

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Revealing the vectors of cellular identity with single-cell genomics

In this article, we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multiscale perspective. The approach comprises three key elements: encoder, fusion strategy, and decoder, respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. First, the source images are fed into the encoder to extract multiscale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available data sets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluations. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse .

NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models

to the editor—The coronavirus disease 2019 (COVID-19) pandemic has brought not only far too many losses of human lives but an economic crisis as well. Thus, effective treatments and vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed. Eyal et al have discussed challenge studies [1] to accelerate the assessment of vaccine effectiveness. In a challenge study, all participants are vaccinated with placebo or with the test vaccine and are then intentionally exposed to doses of SARS-CoV-2. There is already a worldwide initiative to register volunteers for such studies [2]. As all participants have been exposed, the effectiveness of a vaccine can be assessed with smaller sample sizes and possibly more quickly compared to the conventional trial with community participants; however, challenge studies are accompanied by serious ethical issues [3]. First, the characteristics of a distinct group of volunteers without risks for fatal progression or serious late complications of COVID-19 need to be reliably known. Second, a highly effective and safe treatment should be available for patients with COVID-19, so that fatalities and persistent adverse consequences can be avoided. Both these problems are not yet solved. Third, the consent of the volunteer must be with their full understanding of comprehensive information, including appreciation of potential long-term consequences. It may be questioned, however, whether someone at age 20 years or so can imagine the consequences of a scarred lung occurring many years later. Finally, it cannot be taken for granted that a vaccine that works in a challenge study with young, healthy volunteers will work in elderly patients with possible comorbid conditions [4]. Thus, even the social value of challenge trials can be questioned. Fortunately, there is an ethically more acceptable alternative for an accelerated evaluation of SARS-CoV-2 vaccines. The large, simple, randomized trial (LSRT), as proposed by Yusuf et al in 1984, is a reliable, methodologically and ethically sound alternative [5]. Characteristics of this design include: wide, simple eligibility criteria; central randomization; recording of only few baseline data; simple and short-term treatment; reduced or no follow-up visits; and outcome assessments of hard endpoints, preferably by registries. Using this design, truly large randomized trials with sample sizes beyond 40 000 patients have been carried out and answered important questions [6]. Probably due to the increasing bureaucratization of clinical research activities, including very costly monitoring, in the last 20 years there has been an almost complete disappearance of this trial design. At the current stage of evaluation of the effectiveness of SARS-CoV-2 vaccines, this design should be revived as it is ideally suited for this task. There would be wide eligibility criteria with very few exclusion criteria as the vaccine should become available for almost everybody. The investigational vaccine is a 1 or 2-time treatment only and no follow-up visits are needed. The outcomes, COVID-19 or death, can be collected either by registries available in many countries (eg, in the United States, United Kingdom, or Scandinavia) or by patient reporting. Vaccine safety information can be collected by established systems like the Vaccine Safety Datalink in the United States [7], prescription event monitoring programs (eg, the Drug Surveillance Research Unit in the United Kingdom [8]), or by direct patient safety reporting on websites, including those accessible with smartphones [9], which can be specifically designed for vaccine trials. Among the advantages of using the LSRT design are that it allows central randomization of large numbers of volunteers within a short time and rapid collection of the relevant outcomes at a low cost compared to the conventional phase 3 trials with many follow-up visits and extensive monitoring. Adaptive design features (eg, modification of the eligibility criteria considering the accruing safety information) are feasible as well. Given the wide entry criteria, the results provide external validity for large parts of the population compared to any challenge trial, which would need to focus on participants with extremely low risks for developing serious COVID-19. As there will be very many people who would like to participate in such a vaccination trial, the sample sizes needed should be achieved within a very short time. When the LSRT double-blind design is used, the validity of the results is assured and it does not generate the serious ethical issues inherent in challenge trials. Regarding the Salk vaccine, large randomized trials with sample sizes of more than 70 000 were done in the early 1950s [10] and such LSRTs should be feasible in 2020. Thus, the sponsors of vaccine trials and the drug regulatory agencies should start the preparatory work now to be ready once an investigational vaccine is ready to be administered on a large scale.

Journal of infectious diseases

Objective: The aim of the Acromegaly Consensus Group was to revise and update the consensus on diagnosis and treatment of acromegaly comorbidities last published in 2013.Participants: The Consensus Group, convened by 11 Steering Committee members, consisted of 45 experts in the medical and surgical management of acromegaly. The authors received no corporate funding or remuneration.Evidence: This evidence-based consensus was developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to describe both the strength of recommendations and the quality of evidence following critical discussion of the current literature on the diagnosis and treatment of acromegaly comorbidities.Consensus Process: Acromegaly Consensus Group participants conducted comprehensive literature searches for English-language papers on selected topics, reviewed brief presentations on each topic, and discussed current practice and recommendations in breakout groups. Consensus recommendations were developed based on all presentations and discussions. Members of the Scientific Committee graded the quality of the supporting evidence and the consensus recommendations using the GRADE system.Conclusions: Evidence-based approach consensus recommendations address important clinical issues regarding multidisciplinary management of acromegaly-related cardiovascular, endocrine, metabolic, and oncologic comorbidities, sleep apnea, and bone and joint disorders and their sequelae, as well as their effects on quality of life and mortality.

/pdf/a-consensus-on-the-diagnosis-and-treatment-of-acromegaly-1jjbcztvx4.pdf

A Consensus on the Diagnosis and Treatment of Acromegaly Comorbidities: An Update.

Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas12a (Cpf1) has emerged as an effective genome editing tool in many organisms. Here, we developed and optimized a CRISPR-Cas12a-assisted recombineering system to facilitate genetic manipulation in bacteria. Using this system, point mutations, deletions, insertions, and gene replacements can be easily generated on the chromosome or native plasmids in Escherichia coli, Yersinia pestis, and Mycobacterium smegmatis Because CRISPR-Cas12a-assisted recombineering does not require introduction of an antibiotic resistance gene into the chromosome to select for recombinants, it is an efficient approach for generating markerless and scarless mutations in bacteria.IMPORTANCE The CRISPR-Cas9 system has been widely used to facilitate genome editing in many bacteria. CRISPR-Cas12a (Cpf1), a new type of CRISPR-Cas system, allows efficient genome editing in bacteria when combined with recombineering. Cas12a and Cas9 recognize different target sites, which allows for more precise selection of the cleavage target and introduction of the desired mutation. In addition, CRISPR-Cas12a-assisted recombineering can be used for genetic manipulation of plasmids and plasmid curing. Finally, Cas12a-assisted recombineering in the generation of point mutations, deletions, insertions, and replacements in bacteria has been systematically analyzed. Taken together, our findings will guide efficient Cas12a-mediated genome editing in bacteria.

CRISPR-Cas12a–assisted recombineering in bacteria

The field of multimodal medical image fusion has made huge progress in the past decade. However, previous methods always suffer from color distortion , blurring , and noise . To address these problems, we propose a novel Laplacian redecomposition (LRD) framework tailored to multimodal medical image fusion in this article. The proposed LRD has two technical innovations. First, we present a Laplacian decision graph decomposition scheme with image enhancement to obtain complementary information, redundant information, and low-frequency subband images. Second, considering the heterogeneous characteristics of redundant and complementary information, we introduce the concept of the overlapping domain (OD) and non-OD (NOD), where the OD contributes to fuse redundant information while the NOD is responsible for fusing complementary information. In addition, an inverse redecomposition scheme is given by leveraging the global decision graph and local mean to reconstruct high-frequency subband fusion images. Finally, the inverse Laplacian transform is applied to generate the fusion result. Experimental results demonstrate that the proposal outperforms other current popular fusion methods qualitatively and quantitatively.

Laplacian Redecomposition for Multimodal Medical Image Fusion

Glioblastoma (GBM) is the most common brain tumor with significant morbidity and mortality. Autophagy plays a vital role in GBM development and progression. We aimed to establish an autophagy-related multigene expression signature for individualized prognosis prediction in patients with GBM. Differentially expressed autophagy-related genes (DE-ATGs) in GBM and normal samples were screened using TCGA. Univariate and multivariate Cox regression analyses were performed on DE-ATGs to identify the optimal prognosis-related genes. Consequently, NRG1 (HR=1.142, P=0.008), ITGA3 (HR=1.149, P=0.043), and MAP1LC3A (HR=1.308, P=0.014) were selected to establish the prognostic risk score model and validated in the CGGA validation cohort. GSEA revealed that these genes were mainly enriched in cancer- and autophagy-related KEGG pathways. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P= 6.955×10-5). The autophagy signature was identified as an independent prognostic factor. Finally, a prognostic nomogram including the autophagy signature, age, pharmacotherapy, radiotherapy, and IDH mutation status was constructed, and TCGA/CGGA-based calibration plots indicated its excellent predictive performance. The autophagy-related three-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for GBM. The prognostic nomogram could assist individualized survival prediction and improve treatment strategies.

/pdf/development-and-validation-of-a-nomogram-with-an-autophagy-77p7m3lel0.pdf

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Pituitary abscess (PA) is rare and commonly described in case reports or small case series. This study aimed to better determine salient clinical manifestations related to the diagnosis and appropriate treatment of PA using by far the largest case series of this disease. A total of 6361 consecutive patients underwent surgery for pituitary diseases in Peking Union Medical College Hospital between January 1991 and December 2013. Among this cohort, sixty-six patients were diagnosed with PA based on both intraoperative findings and postoperative histopathological evidence. The most common clinical presentation was anterior pituitary hypofunction (81.8 %), followed by common headache (69.7 %), diabetes insipidus (DI; 47.9 %), visual disturbances (45.5 %) and symptoms related to infection (43.9 %). Forty patients (66.7 %) showed typical rim enhancement after gadolinium injection on magnetic resonance imaging (MRI). Most patients (63 of 66, 95.5 %) underwent transsphenoidal surgery (TSS), and the remaining 3 patients (4.5 %) were treated with transcranial surgery (TC). After follow-up for 7.2 ± 4.9 years, PA was diminished in most patients (81.8 %), as demonstrated by post-operative MRI, and eight patients underwent at least one additional operation due to recurrence. For patients with hypopituitarism and DI with rim enhancement on MRI, we should consider the possible diagnosis of PA. Proper use of antibiotics, complete drainage via surgery and hormone replacement for hypopituitarism are the key treatments for PA.

Xiaopeng Guo

Papers

CRISPR-Cas12a–assisted recombineering in bacteria

Laplacian Redecomposition for Multimodal Medical Image Fusion

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Pituitary abscess: clinical manifestations, diagnosis and treatment of 66 cases from a large pituitary center over 23 years